Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211100290-5.doi: 10.11896/jsjkx.211100290

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Facial Landmark Fast Detection Based on Improved YOLOv4-tiny

FU Bo-wen1, LI Chuang-chuang1, LIANG Ai-hua2   

  1. 1 School of Robotics,Beijing Union University,Beijing 100101,China
    2 Frontier Intelligent Technology Research Institute,Beijing Union University,Beijing 100101,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:FU Bo-wen,born in 2000,undergra-duate.His main research interests include computer vison and so on.
    LIANG Ai-hua,born in 1979,Ph.D,associate professor.Her main research interests include biometric recognition and image processing.
  • Supported by:
    National Natural Science Foundation of China(61502036),Scientific Research Project of Beijing Union University(ZK50202002) and General Project of Beijing Association of Higher Education(YB202175).

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Abstract: Facial landmark detection is an important part of face recognition,which has been a hot issue in the field of computer vision.In order to meet the needs of efficient and lightweight face recognition,this paper proposes a facial landmark detection algorithm based on improved YOLOv4-tiny.608*608*3 color image is used for model input.The CSPDarknet53-tiny network is adopted to extract the main features of the input image.Then the extracted features are up-sampled and fused.Attention mechanism is added before feature fusion to improve the detection accuracy.The loss function of YOLOv4-tiny target detection is reconstructed,and the loss function of facial landmark is added to realize the location of facial landmark while detecting.The model output includes face marker frame and five key points.Compared with other facial landmark detection algorithms,the proposed algorithm has higher recognition efficiency and lower configuration requirements while ensuring recognition accuracy.Therefore,it can be better deployed on edge devices or mobile devices.

Key words: Facial landmark detection, YOLOv4-tiny, Attention mechanism, Real-time detection, Deep learning

CLC Number: 

  • TP391
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